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Aboseif, E and Hanna, A S (2024) Benchmarking of construction projects performance for comparative assessment and performance improvement: a statistical quantitative approach. Engineering, Construction and Architectural Management, 31(07), 2829-51.
Al-Hadidi, S, Sweis, G, Abu-Khader, W, Abu-Rumman, G and Sweis, R (2024) Managing future urbanization growth patterns using genetic algorithm modeling. Engineering, Construction and Architectural Management, 31(07), 2648-68.
Delgado-Hernández, D J and Palacios-Navarro, U J (2024) A Bayesian network for selecting improvement management tools to increase customer satisfaction in the construction industry: case study of Mexico. Engineering, Construction and Architectural Management, 31(07), 2900-15.
Gibson, G E, El Asmar, M, Yussef, A and Ramsey, D (2024) A novel approach for measuring the accuracy of front end engineering design. Engineering, Construction and Architectural Management, 31(07), 2669-96.
Guo, J, Xiang, P, Liu, Q and Luo, Y (2024) Transportation infrastructure network optimisation based on computer simulation using Sichuan Province, China as an example. Engineering, Construction and Architectural Management, 31(07), 2740-69.
Khan, S, Panuwatwanich, K and Usanavasin, S (2024) Integrating building information modeling with augmented reality: application and empirical assessment in building facility management. Engineering, Construction and Architectural Management, 31(07), 2809-28.
Khanapure, V U and Shastri, S S (2024) A sustainable–lean implementation methodology: a catalyst for sustainability enhancement of high-rise residential projects. Engineering, Construction and Architectural Management, 31(07), 2585-603.
Kum, Y T, Yap, J B H, Lew, Y L and Lee, W P (2024) Transforming construction health and safety management during COVID-19 pandemic using innovative technologies: PLS-SEM approach. Engineering, Construction and Architectural Management, 31(07), 2770-808.
Lad, V H, Patel, D A, Chauhan, K A and Patel, K A (2024) Prioritisation of bridge for improving its resilience using multi-criteria decision-making techniques. Engineering, Construction and Architectural Management, 31(07), 2952-76.
Li, T, Li, Z and Cai, J (2024) Antecedents and consequence of intra-organizational diffusion of prefabricated construction technology: a contingency theory perspective. Engineering, Construction and Architectural Management, 31(07), 2932-51.
Li, X, Chang, C T, Zhu, Y Y and Li, L L (2024) Key influence factors on the evacuation route selection for fire emergencies in urban underground complexes. Engineering, Construction and Architectural Management, 31(07), 2630-47.
Lin, Q, Zhang, C, Cai, H, Li, X and Xiao, H (2024) Urban night lighting evaluation system and case study: blending popular contemporary elements, cultural traditions and advanced lighting technologies. Engineering, Construction and Architectural Management, 31(07), 2916-31.
Lu, Y, Liu, J and Yu, W (2024) Social risk analysis for mega construction projects based on structural equation model and Bayesian network: a risk evolution perspective. Engineering, Construction and Architectural Management, 31(07), 2604-29.
Masood, R, Roy, K, González, V A, Lim, J B P and Nasir, A R (2024) A supply chain perspective of prefabricated housebuilding diffusion in New Zealand. Engineering, Construction and Architectural Management, 31(07), 2697-720.
Tennakoon, G A, Rameezdeen, R and Chileshe, N (2024) Walking the talk towards sustainable consumption: interventions to promote the uptake of reprocessed construction materials. Engineering, Construction and Architectural Management, 31(07), 2878-99.
Wang, Z, Shi, L and Cui, H (2024) Effects of the host-country institutional environment on the operation mode change of international construction enterprises. Engineering, Construction and Architectural Management, 31(07), 2721-39.
Xie, L L, Chen, Y, Wu, S, Chang, R D and Han, Y (2024) Knowledge extraction for solving resource-constrained project scheduling problem through decision tree. Engineering, Construction and Architectural Management, 31(07), 2852-77.
Zhou, X, Wang, Y, Zhang, Y and Liu, F (2024) Macro investigation on China's engineering insurance industry: based on industrial organization theories. Engineering, Construction and Architectural Management, 31(07), 2977-94.
- Type: Journal Article
- Keywords: china's engineering insurance; dea bootstrap; gmm; the industrial organization hypotheses
- ISBN/ISSN:
- URL: https://doi.org/10.1108/ECAM-03-2022-0219
- Abstract:
Purpose: In China, engineering insurance has been questioned as not being beneficial as expected. This paper seeks to further understand how China's engineering insurance industry functions and to provide a macro perspective explanation for engineering insurance's underdevelopment. Design/methodology/approach: Three industrial organization hypotheses were extended to the engineering insurance context: structure conduct performance (SCP), relative market power (RMP) and efficiency structure (ES) hypotheses. This paper employed the Generalized Method of Moments (GMM) and Data Envelopment Analysis (DEA) bootstrap to test the hypotheses using panel data from 2008 to 2017. Findings: The results suggest that the SCP paradigm is validated in China's engineering insurance market, indicating a concentrated market where the welfare of consumers (e.g. owners, contractors and designers) may be eroded. Several factors are identified to have significant impacts on engineering insurers' performance, such as the investment return, percentage of engineering business, the ratio of outstanding claims, the number of large contractors, market rivalry and entry barriers. Originality/value: Despite the sheer size of China's construction industry and the urgent need to improve risk management, the insurance industry that serves construction firms engineering insurance is underdeveloped. Engineering insurance is yet to be understood from a macro perspective, which may reveal the underlying reasons for engineering insurance's underdevelopment. The industrial organization theories provided a theoretical framework to test the functioning of this specific industry. The disaggregated data (engineering line specific) is employed to ensure effective regulation and policymaking. © 2023, Emerald Publishing Limited.